Python是一种面向对象的解释型计算机程序设计语言,在设计中注重代码的可读性,同时也是一种功能强大的通用型语言。

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这本书的目的是教你像计算机科学家一样思考。这种思维方式结合了数学、工程和自然科学的一些最好的特点。像数学家一样,计算机科学家使用形式语言来表示思想(特别是计算)。就像工程师一样,他们设计东西,将组件组装成系统,并在备选方案中进行权衡。像科学家一样,他们观察复杂系统的行为,形成假设,并测试预测。

计算机科学家最重要的技能是解决问题。解决问题的能力是指组织问题,创造性地思考解决方案,并清晰准确地表达解决方案的能力。事实证明,学习编程的过程是练习解决问题技巧的绝佳机会。这就是为什么这一章叫做,程序之道。

在某种程度上,你将学习编程,这本身就是一项有用的技能。在另一个层面上,您将把编程作为达到目的的一种手段。随着时间的推移,这个目标会越来越清晰。

《如何像计算机科学家一样思考》(How to Think Like a Computer Scientist)已经是目前最好的Python入门书籍了,但我们将其重新出版:

  • 使用在线IDE Replit,而不是向学生展示如何在Windows上设置Python(许多有抱负的程序员都放弃了这一点)。

  • 使展示现代化,并将源代码转换为Markdown,这样读者可以更容易地作出贡献。

  • 使用在线应用程序PythonTutor.com逐步可视化和逐步执行代码。

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Machine learning is revolutionizing image-based diagnostics in pathology and radiology. ML models have shown promising results in research settings, but their lack of interoperability has been a major barrier for clinical integration and evaluation. The DICOM a standard specifies Information Object Definitions and Services for the representation and communication of digital images and related information, including image-derived annotations and analysis results. However, the complexity of the standard represents an obstacle for its adoption in the ML community and creates a need for software libraries and tools that simplify working with data sets in DICOM format. Here we present the highdicom library, which provides a high-level application programming interface for the Python programming language that abstracts low-level details of the standard and enables encoding and decoding of image-derived information in DICOM format in a few lines of Python code. The highdicom library ties into the extensive Python ecosystem for image processing and machine learning. Simultaneously, by simplifying creation and parsing of DICOM-compliant files, highdicom achieves interoperability with the medical imaging systems that hold the data used to train and run ML models, and ultimately communicate and store model outputs for clinical use. We demonstrate through experiments with slide microscopy and computed tomography imaging, that, by bridging these two ecosystems, highdicom enables developers to train and evaluate state-of-the-art ML models in pathology and radiology while remaining compliant with the DICOM standard and interoperable with clinical systems at all stages. To promote standardization of ML research and streamline the ML model development and deployment process, we made the library available free and open-source.

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